Energy Management of Fuel Cell Hybrid Electric Vehicles in Continuous Signal Light Scenarios Based on Model Predictive Control With a Cooperative Optimization Algorithm

With the development of cutting‐edge technologies, vehicle energy optimization is embracing new development opportunities. In this study, a joint optimization strategy utilizing the model predictive control (MPC) and crested porcupine optimizer (CPO) is proposed for the speed planning and energy management of fuel cell hybrid electric vehicles (FCHEVs) in multi‐signal road segments. In the context of Vehicle‐to‐Infrastructure communication, vehicle speed planning is merged with MPC. A multi‐objective convex cost function is formulated based on the reference vehicle speed, and the weight coefficients in the cost function are adjusted through the CPO. Subsequently, the optimal vehicle speed trajectory is produced by using MPC. Simultaneously, MPC is also utilized to address energy management issues. When formulating the energy management objective function, the consumption of hydrogen and the loss of fuel cell (FC) life are considered to enhance the system's economic efficiency. CPO is also employed to optimize the weight coefficients in the cost function. Simulation results for the scenario of six traffic lights indicate that the energy management strategy proposed in this study, based on MPC, reduces hydrogen consumption by 5.7% compared to the traditional Pontryagin's Minimum Principle (PMP), and the output power fluctuation of the FC is minor.